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Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)‐based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three‐layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three‐layer composite generally provided lower overall losses for the training samples, a higher F1‐score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN‐based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN‐based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three‐layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks. Plain Language Summary: Characteristics of the land surface, such as steepness, relative slope position (e.g., ridge vs. valley), and roughness, can be digitally represented using a variety of methods. These digital representations, along with human‐annotated labels, can be provided to artificial intelligence algorithms to generate maps of landforms, such as those associated with river systems, glaciers, or human‐induced changes to the landscape. Given the large number of terrain derivatives that can be generated from digital elevation data, it is unclear how the chosen inputs impact the utility of the resulting maps. This study suggests that artificial intelligence mapping algorithms are sensitive to representations provided to them since different inputs resulted in varying map accuracies. We suggest a combination of features, which collectively describe relative slope position, steepness, and local terrain texture, as a means to represent the landscape and to serve as input to artificial intelligence algorithms. Our results can make it easier to train artificial intelligence algorithms to consistently and objectively find surficial features of interest across large swaths of Earth's surface. Key Points: We test the sensitivity of convolutional neural network geomorphic feature extraction approaches to the input feature spaceA three‐layer composite incorporating slope and topographic position index outperformed common inputs like hillshades and slopeshadesResults held true across four use cases including both natural and anthropogenic landforms [ABSTRACT FROM AUTHOR]

Passive margin stratigraphy contains time‐integrated records of landscapes that have long since vanished. Quantitatively reading the stratigraphic record using coupled landscape evolution and stratigraphic forward models (SFMs) is a promising approach to extracting information about landscape history. However, there is no consensus about the optimal form of simple SFMs because there has been a lack of direct tests against observed stratigraphy in well‐constrained test cases. Specifically, the extent to which SFM behaviour over geologic space and timescales should be governed by local (downslope sediment flux depends only on local slope) versus nonlocal (sediment flux depends on factors other than local slope, such as the history of slopes experienced along a transport pathway) processes is currently unclear. Here, we develop a nonlocal, nonlinear SFM that incorporates slope bypass and long‐distance sediment transport, both of which have been previously identified as important model components but not thoroughly tested. Our model collapses to the local, linear model under certain parameterizations such that best‐fit parameter values can indicate optimal model structure. Comparing 2‐D implementations of both models against seven detailed seismic sections from the Southeast Atlantic Margin, we invert the stratigraphic data for best‐fit model parameter values and demonstrate that best‐fit parameterizations are not compatible with the local, linear diffusion model. Fitting observed stratigraphy requires parameter values consistent with important contributions from slope bypass and long‐distance transport processes. The nonlocal, nonlinear model yields improved fits to the data regardless of whether the model is compared against only the modern bathymetric surface or the full set of seismic reflectors identified in the data. Results suggest that processes of sediment bypass and long‐distance transport are required to model realistic passive margin stratigraphy and are therefore important to consider when inverting the stratigraphic record to infer past perturbations to source regions. [ABSTRACT FROM AUTHOR]

Bedrock landslides shape topography and mobilize large volumes of sediment. Yet, interactions between landslide‐produced sediment and fluvial systems that together govern large‐scale landscape evolution are not well understood. To explain morphological patterns observed in steep, landslide‐prone terrain, we explicitly model stochastic landsliding and associated sediment dynamics. The model accounts for several common landscape features such as slope frequency distributions, which include values in excess of regional stability limits, quasi‐planar hillslopes decorated with straight, closely spaced channel‐like features, and accumulation of sediment in valley networks rather than on hillslopes. Stochastic landsliding strongly affects the magnitude and timing of sediment supply to the fluvial system. We show that intermittent sediment supply is ultimately reflected in topography. At dynamic equilibrium, landslide‐derived sediment pulses generate persistent landscape dynamism through the formation and breaching of landslide dams and epigenetic gorges as landslides force shifts in channel positions. Our work highlights the importance of interactions between landslides and sediment dynamics that ultimately control landscape‐scale response to environmental change. Plain Language Summary: Landslides are natural hazards that predominantly occur where geologic forces have created steep, rugged terrain. Yet from a geologic perspective, landsliding is also one of the forces that contributes to shaping the landscape. Gaining insight into the role of landslides in shaping topography is critical to better understand the interplay between the factors driving long‐term landscape evolution, and ultimately controlling landslide hazard. Here, we use a computer simulation model to study how landslides help shape terrain. Simulating landslides enables us to identify the origins of several common features in mountainous topography. Model results illustrate how repeated landsliding can cause rivers and ridgelines to gradually but continually shift position over time, such that some mountain drainages grow while others shrink. This type of computer model, which describes how topography and landsliding influence one another, can help the scientific community understand the response of steep terrain to environmental changes, such as those related to climate and earthquakes. Key Points: Over geological timescales, landslides produce diagnostic topographic signatures that can be detected in real‐world topographic dataStochastic landsliding results in slopes exceeding stability angles and quasi‐planar hillslopes decorated with closely spaced channelsThe combination of landslide stochasticity and channel‐hillslope feedback mechanisms causes persistent landscape dynamism [ABSTRACT FROM AUTHOR]

Constraining Earth's sediment mass balance over geologic time requires a quantitative understanding of how landscapes respond to transient tectonic perturbations. However, the mechanisms by which bedrock lithology governs landscape response remain poorly understood. Rock type influences the size of sediment delivered to river channels, which controls how efficiently rivers respond to tectonic forcing. The Mendocino triple junction region of northern California, USA, is one landscape in which large boulders, delivered by hillslope failures to channels, may alter the pace of landscape response to a pulse of rock uplift. Boulders frequently delivered by earthflows in one lithology, the Franciscan mélange, have been hypothesized to steepen channels and slow river response to rock uplift, helping to preserve high-elevation, low-relief topography. Channels in other units (the Coastal Belt and the Franciscan schist) may experience little or no erosion inhibition due to boulder delivery. Here we investigate spatial patterns in channel steepness, an indicator of erosion resistance, and how it varies between mélange and non-mélange channels. We then ask whether lithologically controlled boulder delivery to rivers is a possible cause of steepness variations. We find that mélange channels are steeper than Coastal Belt channels but not steeper than schist channels. Though channels in all units steepen with increasing proximity to mapped hillslope failures, absolute steepness values near failures are much higher (~2x) in the mélange and schist than in Coastal Belt units. This could reflect reduced rock erodibility or increased erosion rates in the mélange and schist, or disproportionate steepening due to enhanced boulder delivery by hillslope failures in those units. To investigate the possible influence of lithology-dependent boulder delivery, we map boulders at failure toes in the three units. We find that boulder size, frequency, and concentration are greatest in mélange channels and that Coastal Belt channels have the lowest concentrations. Using our field data to parameterize a mathematical model for channel slope response to boulder delivery, we find that the modeled influence of boulders in the mélange could be strong enough to account for some observed differences in channel steepness between lithologies. At the landscape scale, we lack the data to fully disentangle boulder-induced steepening from that due to spatially varying erosion rates and in situ rock erodibility. However, our boulder mapping and modeling results suggest that lithology-dependent boulder delivery to channels could retard landscape adjustment to tectonic forcing in the mélange and potentially also in the schist. Boulder delivery may modulate landscape response to tectonics and help preserve high-elevation, low-relief topography at the Mendocino triple junction and elsewhere. [ABSTRACT FROM AUTHOR]

Long‐term erosion can threaten infrastructure and buried waste, with consequences for management of natural systems. We develop erosion projections over 10 ky for a 5 km2 watershed in New York, USA. Because there is no single landscape evolution model appropriate for the study site, we assess uncertainty in projections associated with model structure by considering a set of alternative models, each with a slightly different governing equation. In addition to model structure uncertainty, we consider the following uncertainty sources: selection of a final model set; each model's parameter values estimated through calibration; simulation boundary conditions such as the future incision of downstream rivers and future climate; and initial conditions (e.g., site topography which may undergo near‐term anthropogenic modification). We use an analysis‐of‐variance approach to assess and partition uncertainty in projected erosion into the variance attributable to each source. Our results suggest one sixth of the watershed will experience erosion exceeding 5 m in the next 10 ky. Uncertainty in projected erosion increases with time, and the projection uncertainty attributable to each source manifests in a distinct spatial pattern. Model structure uncertainty is relatively low, which reflects our ability to constrain parameter values and reduce the model set through calibration to the recent geologic past. Beyond site‐specific findings, our work demonstrates what information prediction‐under‐uncertainty studies can provide about geomorphic systems. Our results represent the first application of a comprehensive multi‐model uncertainty analysis for long‐term erosion forecasting. Plain Language Summary: Erosion of ground material is a hazard to buildings and other infrastructure, and can pose an environmental risk when it occurs in areas such as radioactive waste repositories and post‐industrial sites. We make projections for erosion over the next 10,000 years and assess uncertainty sources at a 5 km2 watershed in New York state. Natural systems, like the study site, are not as well understood as engineered systems. However, information from studies like this one can provide useful insights to guide management decisions. Parts of the watershed experienced up to 50 m of erosion over the past 13,000 years. The type of model used to simulate land surface evolution over thousands of years is a Landscape Evolution Model (LEM). We use a set of alternative LEMs identified by prior work as successful in simulating this watershed from 13,000 years ago to the present. We consider uncertainty in future climate, incision of downstream rivers, the models used and their parameters, and small changes to the initial topography. We find that just under 20% of the study site will experience erosion exceeding 5 m at +10,000 years. The most important uncertainty source varies across the watershed and increases with time. Key Points: Projections of erosion to 10 ky and uncertainty assessment demonstrate that the dominant uncertainty sources vary in space and timeTwo alternative approaches to model selection result in similar projections and uncertainty at 10 kyVariation across alternative models is subtle and initial conditions influence the location of gullies that drain low‐relief areas [ABSTRACT FROM AUTHOR]

Landslides are the main source of sediment in most mountain ranges. Rivers then act as conveyor belts, evacuating landslide-derived sediment. Sediment dynamics are known to influence landscape evolution through interactions among landslide sediment delivery, fluvial transport and river incision into bedrock. Sediment delivery and its interaction with river incision therefore control the pace of landscape evolution and mediate relationships among tectonics, climate and erosion. Numerical landscape evolution models (LEMs) are well suited to study the interactions among these surface processes. They enable evaluation of a range of hypotheses at varying temporal and spatial scales. While many models have been used to study the dynamic interplay between tectonics, erosion and climate, the role of interactions between landslide-derived sediment and river incision has received much less attention. Here, we present HyLands, a hybrid landscape evolution model integrated within the TopoToolbox Landscape Evolution Model (TTLEM) framework. The hybrid nature of the model lies in its capacity to simulate both erosion and deposition at any place in the landscape due to fluvial bedrock incision, sediment transport, and rapid, stochastic mass wasting through landsliding. Fluvial sediment transport and bedrock incision are calculated using the recently developed Stream Power with Alluvium Conservation and Entrainment (SPACE) model. Therefore, rivers can dynamically transition from detachment-limited to transport-limited and from bedrock to bedrock–alluvial to fully alluviated states. Erosion and sediment production by landsliding are calculated using a Mohr–Coulomb stability analysis, while landslide-derived sediment is routed and deposited using a multiple-flow-direction, nonlinear deposition method. We describe and evaluate the HyLands 1.0 model using analytical solutions and observations. We first illustrate the functionality of HyLands to capture river dynamics ranging from detachment-limited to transport-limited conditions. Second, we apply the model to a portion of the Namche Barwa massif in eastern Tibet and compare simulated and observed landslide magnitude–frequency and area–volume scaling relationships. Finally, we illustrate the relevance of explicitly simulating landsliding and sediment dynamics over longer timescales for landscape evolution in general and river dynamics in particular. With HyLands we provide a new tool to understand both the long- and short-term coupling between stochastic hillslope processes, river incision and source-to-sink sediment dynamics. [ABSTRACT FROM AUTHOR]

Despite considerable community effort, there is no general set of equations to model long‐term landscape evolution. In order to determine a suitable set of landscape evolution process laws for a site where postglacial erosion has incised valleys up to 50 m deep, we generate a set of alternative models and perform a multimodel analysis. The most basic model we consider includes stream power channel incision, uniform lithology, hillslope transport by linear diffusion, and surface‐water discharge proportional to drainage area. We systematically add one, two, or three elements of complexity to this model from one of four categories: hillslope processes, channel processes, surface hydrology, and representation of geologic materials. We apply methods of formal model analysis to the 37 alternative models. The global Method of Morris sensitivity analysis method is used to identify model input parameters that most and least strongly influence model outputs. Only a few parameters are identified as important, and this finding is consistent across two alternative model outputs: one based on a collection of topographic metrics and one that uses an objective function based on a topographic difference. Parameters that control channel erosion are consistently important, while hillslope diffusivity is important for only select model outputs. Uncertainty in initial and boundary conditions is associated with low sensitivity. Sensitivity analysis provides insight to model dynamics and is a critical step in using model analysis for mechanistic hypothesis testing in landscape evolution theory. Key Points: Multiple landscape evolution models are developed for application in a well‐constrained site undergoing locally rapid (4 m/ka) erosionGlobal sensitivity analysis reveals that uncertainty in initial and boundary conditions has low impact on model output metricsGlobal sensitivity analysis finds that channel erosion parameters dominate and that select metrics are sensitive to hillslope diffusivity [ABSTRACT FROM AUTHOR]

We review select mature geomorphic transport laws for use in temperate ridge and valley landscapes and compile parameter estimates for use in applications. This work is motivated by a case study of sensitivity analysis, calibration, validation, multimodel comparison, and prediction under uncertainty, which required bounding values for parameter ranges. Considered geomorphic transport formulae span hillslope sediment transport, soil production, and erosion by surface water. We compile or derive estimates for the parameters in these transport formulae. Additionally, we address a common challenge—connecting changes in precipitation distribution to changes in effective erodibility—by using a simple hydrologic model and a method to estimate precipitation distribution parameters using commonly available data. While some parameters are reasonably well constrained, others span orders of magnitude. Some, such as soil infiltration capacity, have a direct physical meaning but are challenging to measure on geologically relevant timescales. Through the process of compiling these ranges we identify common challenges in parameter determination. The issue of comparable units derives from considering an exponent as an empirically inferred coefficient rather than as an expression of a fundamental relationship. The issue of appropriate timescales derives from the mismatch between human measurement and geologic timescales. This contribution thus serves both as a practical compilation for applications and as a synthesis of outstanding challenges in parameter selection for geomorphic transport laws. Key Points: We provide a compilation of parameter ranges for mature geomorphic transport laws in terrestrial geomorphologyA simple hydrological model connects changes in the precipitation distribution with erodibility coefficientsCommon challenges include incomparable units and mismatch between observation and simulation timescales [ABSTRACT FROM AUTHOR]

We present a multimodel analysis for mechanistic hypothesis testing in landscape evolution theory. The study site is a watershed with well‐constrained initial and boundary conditions in which a river network locally incised 50 m over the last 13 ka. We calibrate and validate a set of 37 landscape evolution models designed to hierarchically test elements of complexity from four categories: hillslope processes, channel processes, surface hydrology, and representation of geologic materials. Comparison of each model to a base model, which uses stream power channel incision, uniform lithology, hillslope transport by linear diffusion, and surface water discharge proportional to drainage area, serves as a formal test of which elements of complexity improve model performance. Model fit is assessed using an objective function based on a direct difference between observed and simulated modern topography. A hybrid optimization scheme identifies optimal parameters and uncertainty. Multimodel analysis determines which elements of complexity improve simulation performance. Validation tests which model improvements persist when models are applied to an independent watershed. The three most important model elements are (1) spatial variation in lithology (differentiation between shale and glacial till), (2) a fluvial erosion threshold, and (3) a nonlinear relationship between slope and hillslope sediment flux. Due to nonlinear interactions between model elements, some process representations (e.g., nonlinear hillslopes) only become important when paired with the inclusion of other processes (e.g., erosion thresholds). This emphasizes the need for caution in identifying the minimally sufficient process set. Our approach provides a general framework for hypothesis testing in landscape evolution. Key Points: Comparison of calibrated models at a well‐constrained field site serves as a formal hypothesis test of landscape evolution theoryRepresenting subtle lithologic contrasts, fluvial incision thresholds, and nonlinear hillslope transport improves simulation results mostModel structure improvements are not always linearly additive [ABSTRACT FROM AUTHOR]

Geologists frequently debate the origin of iconic river canyons, as well as the extent to which they record climatic and tectonic signals. Fluvial and hillslope processes work in concert to control canyon evolution; rivers both set the boundary conditions for adjoining hillslopes and respond to delivery of hillslope-derived sediment. But, what happens when canyon walls deliver boulders that are too large for a river to carry? River canyons commonly host large blocks of rock derived from resistant hillslope lithologies. Blocks have recently been shown to control the shapes of hillslopes and channels by inhibiting sediment transport and bedrock erosion. Here, we developed the first process-based model for canyon evolution that incorporates the roles of blocks in both hillslope and channel processes. Our model reveals that two-way negative channel-hillslope feedbacks driven by block delivery to the river result in characteristic plan-view and cross-sectional river canyon forms. Internal negative feedbacks strongly reduce the rate at which erosional signals pass through landscapes, leading to persistent local unsteadiness even under steady tectonic and climatic forcing. Surprisingly, while the presence of blocks in the channel initially slows incision rates, the subsequent removal of blocks from the oversteepened channel substantially increases incision rates. This interplay between channel and hillslope dynamics results in highly variable long-term erosion rates. These autogenic channel-hillslope dynamics can mask external signals, such as changes in rock uplift rate, complicating the interpretation of landscape morphology and erosion histories. [ABSTRACT FROM AUTHOR]

Models of landscape evolution provide insight into the geomorphic history of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last 4 decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. Terrainbento provides a set of 28 model programs that implement alternative transport laws related to four process elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs are a systematic subset of the 2048 possible numerical models associated with 11 binary choices. Each binary choice is related to one of these four elements – for example, the use of linear or nonlinear hillslope diffusion. Terrainbento is an extensible framework: base classes that treat the elements common to all numerical models (such as input/output and boundary conditions) make it possible to create a new numerical model without reinventing these common methods. Terrainbento is built on top of the Landlab framework such that new Landlab components directly support the creation of new terrainbento model programs. Terrainbento is fully documented, has 100 % unit test coverage including numerical comparison with analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady-state topography between five different numerical models. [ABSTRACT FROM AUTHOR]

Models of landscape evolution provide insight into the development of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last four decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python modeling package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. terrainbento provides a set of 28 model programs that implement alternative transport laws related to four model elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs stem from 13 binary choices related to one of these four elements -- for example, the use of linear or non-linear hillslope diffusion. terrainbento is an extensible framework: model base classes that treat the elements common to all models (such as input/output and boundary conditions) make it possible to create a new model without re-inventing these common methods. terrainbento is built on top of the Landlab framework, such that new Landlab components directly support the creation of new terrainbento models. terrainbento is fully documented, has 100% unit test coverage including numerical comparison with % all available analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process model theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady state topography between five different process models. [ABSTRACT FROM AUTHOR]

Geomorphologists often rely on simple models of river channel incision for predicting rates of landscape evolution and channel response to perturbations, as well as extracting climatic and tectonic signals from river longitudinal profiles. Recent work has shown that large, hillslope‐derived blocks delivered to rivers may noticeably alter the form and evolution of river profiles from the behavior predicted by the most common models. Here we use a 1‐D model of river reach erosion and hillslope block delivery to explore the conditions under which block delivery strongly influences channel evolution. We use global sensitivity analysis to understand which model parameters most strongly affect the channel longitudinal profile. We explore the effects of blocks on the relationship between erosion rate and channel gradient, and on the erosion rate‐channel steepness exponent ϕ, and find that block effects result in highly variable slope and ϕ over the range of erosion rates and climatic conditions (discharge mean and variability) tested. The influence of blocks on erosion rate‐slope scaling may be approximated by a piecewise model: The erosion threshold imposed by blocks scales linearly with erosion rate when blocks are infrequently mobile and remains constant when blocks are frequently mobile. We explore the implications of this variable‐threshold model for the erosion rate‐channel steepness relationship and find that erosion rate‐dependent thresholds imposed by hillslope‐derived blocks cause significant departures from previous models but may be consistent with existing field data sets. Our work has implications for landscape evolution modeling and the inversion of channel profiles for forcing information. [ABSTRACT FROM AUTHOR]

Quantifying off-fault deformation (OFD) rates on geomorphic time scales (10²-105 yr) along strike-slip faults is critical for resolving discrepancies between geologic and geodetic slip-rate estimates, improving knowledge of seismic hazard, and understanding the influence of tectonic motion on landscapes. Quantifying OFD over these time scales is challenging without displacement markers such as offset terraces or geologic contacts. We present a landscape evolution model coupled with distributed lateral tectonic shear to show how drainage basins sheared by lateral tectonic motion can reveal OFD rates. The model shows that OFD rate can control the orientation of drainage basin topography: the faster the OFD rate, the greater the deflection of drainage basins toward a fault-parallel orientation. We apply the model to the southern San Andreas fault near the Mecca Hills (southern California, USA), where drainage basins change in orientation with proximity to the fault. Comparison of observed and modeled topography suggests that the OFD rate in the Mecca Hills follows an exponential-like spatial pattern with a maximum rate nearest the fault of 3.5 ± 1.5 mm/yr, which decays to ~0 at ~600 m distance from the fault. This rate is applicable since the initiation of differential rock uplift in the Mecca Hills ca. 760 ka. Our results suggest that OFD in this 800 m study area may be as high as 10% of total plate motion. This example demonstrates that curved drainage basins may be used to estimate OFD rates along strike-slip faults. [ABSTRACT FROM AUTHOR]

Models of landscape evolution by river erosion are often either transport-limited (sediment is always available, but may or may not be transportable) or detachment-limited (sediment must be detached from the bed, but is then always transportable). While several models incorporate elements of, or transition between, transport-limited and detachment-limited behavior, most require that either sediment or bedrock, but not both, are eroded at any given time. We present SPACE (Stream Power with Alluvium Conservation and Entrainment) 1.0, a new model for simultaneous evolution of an alluvium layer and a bedrock bed based on conservation of sediment mass both on the bed and in the water column. The model treats sediment transport and bedrock erosion simultaneously, embracing the reality that many rivers (even those commonly defined as "bedrock" rivers) flow over a partially alluviated bed. The SPACE model is a component of the Landlab modeling toolkit, a Python-language library used to create models of earth surface processes. Landlab allows efficient coupling between the SPACE model and components simulating basin hydrology, hillslope evolution, weathering, lithospheric flexure, and other surface processes. Here, we first derive the governing equations of the SPACE model from existing sediment transport and bedrock erosion formulations and explore the behavior of local analytical solutions for sediment flux and alluvium thickness. We derive steady-state analytical solutions for channel slope, alluvium thickness, and sediment flux, and show that SPACE matches predicted behavior in detachment-limited, transport-limited, and mixed conditions. We provide an example of landscape evolution modeling in which SPACE is coupled with hillslope diffusion, and demonstrate that SPACE provides an effective framework for simultaneously modeling 2-D sediment transport and bedrock erosion. [ABSTRACT FROM AUTHOR]

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks. [ABSTRACT FROM AUTHOR]